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A composite logistic regression approach for ordinal panel data regression

 

Table Of Contents


Chapter ONE

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms

Chapter TWO

2.1 Overview of Logistic Regression
2.2 Ordinal Panel Data Analysis
2.3 Composite Regression Models
2.4 Applications of Logistic Regression
2.5 Panel Data Analysis Techniques
2.6 Composite Regression in Social Sciences
2.7 Challenges in Ordinal Panel Data Regression
2.8 Comparison of Regression Methods
2.9 Interpretation of Logistic Regression Results
2.10 Future Trends in Composite Regression

Chapter THREE

3.1 Research Methodology Overview
3.2 Research Design and Approach
3.3 Data Collection Methods
3.4 Sampling Techniques
3.5 Variable Selection and Measurement
3.6 Model Specification
3.7 Data Analysis Procedures
3.8 Validation and Reliability Testing

Chapter FOUR

4.1 Descriptive Analysis of Data
4.2 Regression Model Estimation
4.3 Interpretation of Regression Results
4.4 Comparison of Different Models
4.5 Robustness Checks and Sensitivity Analysis
4.6 Discussion of Findings
4.7 Implications for Theory and Practice
4.8 Recommendations for Future Research

Chapter FIVE

5.1 Summary of Findings
5.2 Conclusions Drawn from the Study
5.3 Contribution to Knowledge
5.4 Practical Implications
5.5 Limitations and Suggestions for Future Research
5.6 Recommendations for Policy and Practice
5.7 Conclusion and Final Remarks

Thesis Abstract

We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001; Hsiao, 2003) can be directly applied. As a result, the new method allows the unobserved subject effects to be correlated with the observed predictors in an arbitrary manner. Computationally, the new method is able to profile out unobserved subject effects in a very neat manner. This not only makes computational implementation very easy but also makes theoretical treatment straightforward. In particular, we show theoretically that the resulting estimator is n-consistent and asymptotically normal. Both simulations and a real example are reported to demonstrate the usefulness of the new method.

Thesis Overview

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